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An empirical study on program failures of deep learning jobs., , , , , и . ICSE, стр. 1159-1170. ACM, (2020)Tux2: Distributed Graph Computation for Machine Learning., , , , , , , и . NSDI, стр. 669-682. USENIX Association, (2017)EasyScale: Elastic Training with Consistent Accuracy and Improved Utilization on GPUs., , , , , , , , , и 2 other автор(ы). SC, стр. 55:1-55:14. ACM, (2023)Crux: GPU-Efficient Communication Scheduling for Deep Learning Training., , , , , , , , и . SIGCOMM, стр. 1-15. ACM, (2024)Multi-residual unit fusion and Wasserstein distance-based deep transfer learning for mill load recognition., , и . Signal Image Video Process., 18 (4): 3187-3196 (июня 2024)BeamRaster: A Practical Fast Massive MU-MIMO System With Pre-Computed Precoders., , , , , , и . IEEE Trans. Mob. Comput., 18 (5): 1014-1027 (2019)Balanced Sparsity for Efficient DNN Inference on GPU., , , , и . AAAI, стр. 5676-5683. AAAI Press, (2019)MLaaS in the Wild: Workload Analysis and Scheduling in Large-Scale Heterogeneous GPU Clusters., , , , , , , , , и . NSDI, стр. 945-960. USENIX Association, (2022)Focusing More on Conflicts with Mis-Predictions Helps Language Pre-Training., , , и . CoRR, (2020)Llumnix: Dynamic Scheduling for Large Language Model Serving., , , , , , и . OSDI, стр. 173-191. USENIX Association, (2024)